ElevenLabs’ early deck claimed its prototype could dub a ten-minute video in two minutes. (Source: supplied deck, slide 5).

This early presentation turns automatic dubbing into a specific customer and business proposition.

Its value for founders is the connection between a costly task, a visible product, and a focused market.

You will learn five ways to make an AI pitch concrete while separating evidence from assumptions.

Below is the full breakdown:

Inside you'll find:

A customer buys relief from a costly task before caring about your model architecture.

Slide 2 describes conventional dubbing at roughly $100 per minute and more than two weeks for a ten-minute video. These are the deck’s historical estimates, not independently established industry averages or current market prices. (Source: supplied deck, slide 2).

The framing is useful because it names a unit, a cost, and a delay. A founder can investigate each one. “Localization is inefficient” leaves the investor guessing what changes if the product succeeds.

→ Define the buyer’s task, its present cost, and the consequence of leaving it unsolved.

For your own pitch, collect recent examples from the segment you want to serve. Separate supplier fees, internal review time, and publication delays. Record what quality level and workflow each example includes.

Keep the comparison fair. The prototype’s two-minute claim does not establish the time needed for translation review, corrections, and final approval. Comparing processing time with a complete production schedule would overstate the evidence.

The business opportunity has later support. YouTube reported in September 2025 that creators uploading multilingual audio received, on average, over 25% of their watch time in a video’s non-primary language, using data as of July 2025. YouTube, September 2025.

That is a share of viewing among participating creators, not a promised 25% uplift or proof of ElevenLabs’ impact. It nevertheless gives the founder a concrete outcome to investigate: whether localization reaches an audience the customer already wants.

End the problem slide with that business consequence. Faster production matters when it enables more useful output, earlier distribution, or a better margin. Explain which one your initial customer will pay for.

A visible workflow can explain the product more clearly than a list of AI capabilities.

Slide 5, shown above, follows the input through subtitles, translation, audio separation, voice generation, and a downloadable video. It also identifies the voice-generation step as core technology.

The reader can see both the user outcome and the component the company emphasizes. (Source: supplied deck, slide 5).

→ Show the input, the work your product performs, and the output the customer can use.

For an AI support product, that might be a customer request, retrieval of account context, a proposed answer, and an approved response. For a document tool, it might be an uploaded contract and a reviewed set of changes.

Use one representative task rather than a tour of every feature. Identify which steps are automated, which require approval, and what happens when the system cannot complete the task.

Then distinguish a demo from a repeatable result. State the input conditions, output quality criteria, manual interventions, and number of attempts. For a speed claim, report processing time separately from review and total turnaround.

ElevenLabs announced its AI Dubbing feature on October 10, 2023, describing translation that preserved the original speaker’s voice.

That later product announcement establishes a launch milestone; it does not independently validate every performance claim in the early deck. ElevenLabs, October 2023.

This distinction also matters when describing traction. The supplied presentation’s traction and timeline slides are redacted. They cannot establish customer adoption, revenue, or a delivery schedule. (Source: supplied deck, slides 11 and 14).

Build your own evidence sequence accordingly: a demo shows the task is possible; repeated tests show consistency; customer use tests whether the result is valuable. Present the strongest evidence you have, and make the next validation step explicit.

A broad technical capability becomes a business through a specific first customer.

Slide 7 marks automatic dubbing for creators as the initial focus, then maps additional uses including offline voice generation, professional dubbing, and real-time communication.

This gives the vision a starting point rather than treating every use case as an immediate priority. (Source: supplied deck, slide 7).

→ Name the first buyer, the recurring task, and the reason that buyer will act now.

The logos in this slide illustrate the proposed landscape. They do not establish customer relationships. A founder should label prospective segments and confirmed customers separately, especially when familiar brands make the opportunity look more mature.

For your own initial segment, describe the person who feels the pain and the person who approves spending. A creator, production agency, and film studio may all need localization while buying through very different processes.

Choose the segment where you can reach buyers, observe repeated use, and test willingness to pay. A smaller reachable group can provide more useful evidence than a large category with no credible route to customers.

Specify the first acquisition experiment. State how you will recruit prospects, what they will try, and what behavior would justify continued investment. Completed onboarding, repeated paid use, and renewal answer different questions.

Treat expansion as a sequence of hypotheses. Moving from recorded videos to live conversation changes latency requirements and failure consequences. Moving to professional dubbing can change review standards and procurement.

Your vision slide should explain which capability carries forward and what new evidence each step requires. Investors can then understand the ambition without assuming that success in the first niche automatically unlocks every adjacent market.

A market estimate becomes useful when a reader can challenge its inputs.

Slide 9 links a proposed segment of 100,000 creators to three monthly videos, ten minutes per video, and three dubbed languages. Multiplication produces nine million translated minutes per month.

At the assumed $1 per minute, that is $108 million annually, rounded to about $110 million. (Source: supplied deck, slide 9).

→ Show the equation, then separate addressable demand from revenue you expect to capture.

This is a demand scenario built from the deck’s assumptions. It is not booked revenue, and the creator counts are not verified current market totals. The arithmetic can be checked even when the commercial inputs still need validation.

There is a useful tension inside the slide: it also identifies an immediate group of 10,000 creators, while the nine-million-minute calculation uses the broader 100,000 group.

Using 10,000 with the same frequency, duration, language count, and price yields $10.8 million annually. This is an illustrative recalculation, not a forecast. It shows why the initial target segment and the larger opportunity should have distinct labels.

Next, test adoption and actual consumption. A creator might localize only selected videos or start with one additional language. Subscription allowances, discounts, and unused capacity can also change realized revenue.

Build separate cases for reachable customers, paid conversion, retained usage, and effective price. Identify which inputs come from observed behavior and which remain hypotheses. Do not hide uncertainty inside an oversized total.

Finally, connect the model to a sales plan. The immediate question is how you acquire the next paying customers and learn whether they repeat. A large market supports ambition; a transparent set of assumptions supports a decision.

A technical diagram needs evidence of customer value.

Slide 13 separates speaker identity, emotional delivery, and linguistic content before recombining them into dubbed speech. That connects the proposed research to an outcome a buyer can recognize: retaining a speaker’s performance across languages. (Source: supplied deck, slide 13).

→ Connect each technical advantage to a measurable customer benefit and a credible comparison.

Current context sharpens the question. ElevenLabs’ May 2026 Dubbing v2 announcement describes conditioning on the original performance across more than 90 languages.

This is the company’s current product claim, not evidence that the early prototype had those capabilities. ElevenLabs, May 2026.

Its documentation also describes a tradeoff between voice similarity and natural delivery across languages. That suggests a useful evaluation: measure the dimensions customers care about rather than declaring one output “human quality.” ElevenLabs dubbing documentation.

For your own AI product, compare representative tasks against the customer’s present alternative. Track acceptance, correction effort, turnaround, and cost per usable output.

Explain how performance improves through assets or learning a competitor cannot immediately reproduce.

Before sharing your pitch, test these five connections:

Rewrite the weakest connection first. Building yourself as a founder includes learning to tell an attractive hypothesis from a demonstrated result. Your pitch becomes stronger when both are clear.